Accurate estimation of Origin and Destination (O-D) of passengers has been an essential objective for public transit agencies because knowledge of passengers’ flow enables them to forecast ridership, and plan for bus schedules, and bus routes. However, obtaining O-D information using traditional ways, such as conducting surveys, cannot fulfill today’s requirements of intelligent transportation and route planning in smart cities. Estimating bus passengers’ O-D using Wi-Fi and Bluetooth signals detected from their mobile devices is the primary objective of this project. For this purpose, we collected anonymized passengers’ data using SMATS TrafficBoxTM sensor provided by “SMATS Traffic Solutions” company. We then performed pre-processing steps including data cleaning, feature extraction, and data normalization, then, built various models using data mining techniques. The main challenge in this project was to distinguish between passengers’ and non-passengers’ signals since the sensor captures all signals in its surrounding environment including substantial noise from devices outside of the bus. To address this challenge, we applied Hierarchical and K-Means clustering algorithms to separate passengers from non-passengers’ signals automatically. By assigning GPS data to passengers’ signals, we could find commuters’ O-D. Moreover, we developed a second method based on an online analysis of sequential data, where specific thresholds were set to recognize passengers’ signals in real time. This method could create the O-D matrix online. Finally, in the validation phase, we compared the ground truth data with both estimated O-D matrices in both approaches and calculated their accuracy. Based on the final results, our proposed approaches can detect more than 20% of passengers (compared to 5% detection rate of traditional survey-based methods), and estimate the origin and destination of passengers with an accuracy of about 93%. With such promising results, these approaches are suitable alternatives for traditional and time-consuming ways of obtaining O-D data. This enables public transit companies to enhance their service offering by efficiently planning and scheduling the bus routes, improving ride comfort, and lowering operating costs of urban transportation.